Understand the Technology Ecosystem of Generative AI
After completing this unit, you’ll be able to:
- Identify key components contributing to rapid generative AI development.
- Describe types of technology that comprise the generative AI tech stack.
- Describe common concerns businesses have about generative AI.
Supercharging Generative AI Training
Generative AI has gained a lot of capabilities in what seems like a very short amount of time. The incredibly fast pace of improvement is largely due to three big factors. The first is the availability of huge amounts of training data. As mentioned in the previous unit, the more than a billion web pages on the internet are a great source of writing samples. But data is only good if you have a way to use it. That’s where the second big change comes in: better training.
As you learn in Artificial Intelligence Fundamentals, researchers design neural networks that use sophisticated math to train AI models. The architecture of neural networks is a field of study that’s constantly progressing. In 2017, researchers at Google published a game-changing paper about training large language models. They proposed a new AI architecture called a transformer. As you can imagine, the details of the research are pretty complicated. But to simplify (greatly), the new architecture was capable of identifying important relationships between words, no matter how far away they appear within a block of text. It could retain that connection even after processing lots of words.
The new transformer architecture brings us to the third major factor in the rapid advancement of generative AI: computational power. It takes a lot of processing power to do the math behind AI model training. Historically, AI models are designed in a way that requires a sequence of calculations, run one after the other. The transformer architecture is different—it relies on many separate, concurrent calculations.
So, one computer processor can do the first calculation while a different processor does the second at the same time. That’s called parallel computing, and it greatly reduces the time it takes to train a transformer. On top of that, in recent years processors that can perform parallel computing have become much more powerful and abundant.
These three factors of data, architecture, and computing have converged for just the right conditions to train very capable large language models. One of the biggest LLMs is the GPT language model, which stands for generative pre-trained transformer. In other words, a model that’s already been trained that can be used to generate text-related content.
Right now, there are already hundreds of sites on the internet where you can go to get hands-on with generative AI. When you visit one of those sites, you’re at the tip of a technology iceberg. And that technology can come from a lot of different sources. Let’s investigate the tech stack that makes it possible to bring awesome generative AI experiences to the masses.
- At the bottom of the iceberg, we start with the compute hardware providers. Training an LLM can take a staggering amount of computational power, even if you're training a transformer. It also takes computing power to process requests to actually use the model after it’s been trained. Technically you can train AI models on any computing hardware, but processors that excel at parallel computing are ideal. Today the biggest name in AI compute is Nvidia.
- Next are the cloud platforms that allow developers to tap into the compute hardware in a cloud deployment model. Devs can rent the appropriate amount of time for a specific project, and the platforms can efficiently distribute requests for computing time across a connected system. Google, Amazon, Microsoft, and Oracle are the main tech providers in this space.
- AI models, including LLMs are the next layer. These models are carefully crafted using research techniques and trained using a combination of public and privately curated data. Developers can connect to LLMs through an application programming interface (API), so they can harness the full power of NLP in their own applications. The trained and accessible AI model is commonly referred to as a foundational model. Because these models are accessed through an API, developers can easily switch from one foundational model to another as needed. A few examples of foundational models are GPT4, Claude, Stable Diffusion, and LLaMA.
- The next layer is infrastructure optimization, which is all about providing tools and services that make for more efficient and higher-quality model training. For example, a service might offer perfectly curated data sets to train on. Another might provide analytics to test the accuracy of generated content. It’s also at this point where foundational models can be fine-tuned with specialized, proprietary data to better meet the needs of a particular company. This is a busy space in the AI ecosystem, with many companies offering a variety of optimization services.
- Finally, we find ourselves back at the tip of the iceberg: the applications. Developers of all kinds can tap into optimization services and foundational models for their apps. Already we see LLM-powered standalone tools, as well as plugins for mainstream applications.
This thriving ecosystem of technology companies has grown at an incredible rate just over the past few years. Some companies will specialize in one particular segment. For example, one in the foundational model space may want to focus on training new and better performing models to differentiate themselves. Other companies will create solutions that span multiple layers of the tech stack, creating their own proprietary LLM to use for their application.
Many businesses are just starting to get a handle on what AI can do for them. Given the unprecedented demand for AI technology, there’s a huge amount of opportunity for businesses to make their mark at several levels of the AI tech stack.
Common Concerns About Generative AI
Generative AI is going to lead to many changes in how we interact with computers. With any disruptive technology, it’s important to understand its limitations and causes for concern. Here are a few of the main concerns with generative AI.
Hallucinations: Remember that generative AI is really another form of prediction, and sometimes predictions are wrong. Predictions from generative AI that diverge from an expected response, grounded in facts, are known as hallucinations. They happen for a few reasons, like if the training data was incomplete or biased, or if the model was not designed well. So with any AI generated text, take the time to verify the content is factually correct.
Data security: Businesses can share proprietary data at two points in the generative AI lifecycle. First, when fine-tuning a foundational model. Second, when actually using the model to process a request with sensitive data. Companies that offer AI services must demonstrate that trust is paramount and that data will always be protected.
Plagiarism: LLMs and AI models for image generation are typically trained on publicly available data. There’s the possibility that the model will learn a style and replicate that style. Businesses developing foundational models must take steps to add variation into the generated content. Also, they may need to curate the training data to remove samples at the request of content creators.
User spoofing: It’s easier than ever to create a believable online profile, complete with an AI generated picture. Fake users like this can interact with real users (and other fake users), in a very realistic way. That makes it hard for businesses to identify bot networks that promote their own bot content.
Sustainability: The computing power required to train AI models is immense, and the processors doing the math require a lot of actual power to run. As models get bigger, so do their carbon footprints. Fortunately, once a model is trained it takes relatively little power to process requests. And, renewable energy is expanding almost as fast as AI adoption!
Generative AI is capable of assisting businesses and individuals alike with all sorts of language-based tasks. The convergence of lots of data, clever AI architecture, and huge amounts of computing power has supercharged generative AI development and the growth of the AI ecosystem.